Regulatory Network Reconstruction Using Stochastic Logical Networks

  • Bartek Wilczyński
  • Jerzy Tiuryn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4210)


This paper presents a method for regulatory network reconstruction from experimental data. We propose a mathematical model for regulatory interactions, based on the work of Thomas et al. [25] extended with a stochastic element and provide an algorithm for reconstruction of such models from gene expression time series. We examine mathematical properties of the model and the reconstruction algorithm and test it on expression profiles obtained from numerical simulation of known regulatory networks. We compare the reconstructed networks with the ones reconstructed from the same data using Dynamic Bayesian Networks and show that in these cases our method provides the same or better results. The supplemental materials to this article are available from the website


Regulatory Network Bayesian Network Negative Feedback Loop Discrete State Boolean Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bartek Wilczyński
    • 1
    • 2
  • Jerzy Tiuryn
    • 2
  1. 1.Institute of MathematicsPolish Academy of SciencesWarszawaPoland
  2. 2.Institute of InformaticsWarsaw UniversityWarszawaPoland

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